Publications
Our teams aspire to make discoveries that impact everyone, and core to our approach is sharing our research and tools to fuel progress in the field.
Our teams aspire to make discoveries that impact everyone, and core to our approach is sharing our research and tools to fuel progress in the field.
Sort By
1 - 15 of 10471 publications
Preview abstract
Many AI applications of interest require specialized multi-modal models. Yet, relevant data for training these models is inherently scarce. Human annotation is prohibitively expensive, error-prone, and time-consuming. Meanwhile, existing synthetic data generation methods often rely on manual prompts, evolutionary algorithms, or extensive seed data from the target distribution - limiting scalability and control. In this paper, we introduce Simula, a novel, seedless framework that balances global and local reasoning to generate synthetic datasets. We utilize taxonomies to capture a global coverage space and use a series of agentic refinements to promote local diversity and complexity. Our approach allows users to define desired dataset characteristics through an explainable and controllable process, without relying on seed data. This unlocks new opportunities for developing and deploying AI in domains where data scarcity or privacy concerns are paramount.
View details
Preview abstract
This tutorial examines the progress and scaling limitations of IM-DD based optical technologies and explores how datacenter use cases optimized coherent technology, including a newly proposed polarization-folding, time-diversity approach and a novel single-sideband coherent detection technology—can address some of these challenges
View details
Snap-it, Tap-it, Splat-it: Tactile-Informed 3D Gaussian Splatting for Reconstructing Challenging Surfaces
Mauro Comi
Max Yang
Jonathan Tremblay
Valts Blukis
Yijiong Lin
Nathan Lepora
Laurence Aitchison
2025
Preview abstract
Touch and vision go hand in hand, mutually enhancing our ability to understand the world. From a research perspective, the problem of mixing touch and vision is underexplored and presents interesting challenges. To this end, we propose Tactile-Informed 3DGS, a novel approach that incorporates touch data (local depth maps) with multi-view vision data to achieve surface reconstruction and novel view synthesis. Our method optimises 3D Gaussian primitives to accurately model the object's geometry at points of contact. By creating a framework that decreases the transmittance at touch locations, we achieve a refined surface reconstruction, ensuring a uniformly smooth depth map. Touch is particularly useful when considering non-Lambertian objects (e.g. shiny or reflective surfaces) since contemporary methods tend to fail to reconstruct with fidelity specular highlights. By combining vision and tactile sensing, we achieve more accurate geometry reconstructions with fewer images than prior methods. We conduct evaluation on objects with glossy and reflective surfaces and demonstrate the effectiveness of our approach, offering significant improvements in reconstruction quality.
View details
PROTECT: A Framework to Foster Digital Resilience for Youth Navigating Technology-Facilitated Abuse
Diana Freed
Natalie Bazarova
Dan Cosley
Patrick Gage Kelley
Social Sciences Journal, 14(6) (2025)
Preview abstract
Youth are increasingly exposed to a broad range of technology-facilitated abuse that challenges their safety and well-being. Building on previous work that examined youth help-seeking behaviors, coping strategies, threats they encounter, and the social support systems around them, we articulate a framework— called PROTECT—Problem recognition, Reaching out, Organizing support, Training, Engaging experts, Continuous support, and Tackling safety measures—which integrates existing models of support, help-seeking, and digital skills to offer a high-level, structured approach to adults who serve as a support system to youth navigate technology-facilitated abuse. The framework unpacks social and contextual dynamics that influence help-seeking behaviors, providing a foundation for educators, advocates, health professionals, developers and other adult stakeholders to design and develop trauma-informed, timely interventions to promote resilience.
View details
Preview abstract
Judging an action’s safety requires knowledge of the context in which the action takes place. To human agents who act in various contexts, this may seem obvious: performing an action such as email deletion may or may not be appropriate depending on the email’s content, the goal (e.g., to erase sensitive emails or to clean up trash), and the type of email address (e.g., work or personal). Unlike people, computational systems have often had only limited agency in limited contexts. Thus, manually crafted policies and user confirmation (e.g., smartphone app permissions or network access control lists), while imperfect, have sufficed to restrict harmful actions. However, with the upcoming deployment of generalist agents that support a multitude of tasks (e.g., an automated personal assistant), we argue that we must rethink security designs to adapt to the scale of contexts and capabilities of these systems. As a first step, this paper explores contextual security in the domain of agents and proposes contextual agent security (Conseca), a framework to generate just-in-time, contextual, and human-verifiable security policies.
View details
Preview abstract
Mainstream artificial neural network models, such as Deep Neural Networks (DNNs) are computation-heavy and energy-hungry. Weightless Neural Networks (WNNs) are natively built with RAM-based neurons and represent an entirely distinct type of neural network computing compared to DNNs. WNNs are extremely low-latency, low-energy, and suitable for efficient, accurate, edge inference. The WNN approach derives an implicit inspiration from the decoding process observed in the dendritic trees of biological neurons, making neurons based on Random Access Memories (RAMs) and/or Lookup Tables (LUTs) ready-to-deploy neuromorphic digital circuits. Since FPGAs are abundant in LUTs, LUT based WNNs are a natural fit for implementing edge inference in FPGAs.
WNNs has been demonstrated to be an energetically efficient AI model, both in software, as well as in hardware. For instance, the most recent DWN – Differential Weightless Neural Network – model demonstrates up to 135× reduction in energy costs in FPGA implementations compared to other multiplication-free approaches, such as binary neural networks (BNNs) and DiffLogicNet, up to 9% higher accuracy in deployments on constrained devices, and culminate in up to 42.8× reduction in circuit area for ultra-low-cost chip implementations. This tutorial will help participants understand how WNNs work, why WNNs were underdogs for such a long time, and be introduced to the most recent members of the WNN family, such as BTHOWeN , LogicWiSARD, COIN, ULEEN and DWN, and contrast to BNNs and LogicNets.
View details
Scalable Private Partition Selection via Adaptive Weighting
Justin Y. Chen
Forty-second International Conference on Machine Learning (2025)
Preview abstract
In the differentially private partition selection problem (a.k.a. private set union, private key discovery), users hold subsets of items from an unbounded universe. The goal is to output as many items as possible from the union of the users' sets while maintaining user-level differential privacy. Solutions to this problem are a core building block for many privacy-preserving ML applications including vocabulary extraction in a private corpus, computing statistics over categorical data and learning embeddings over user-provided items.
We propose an algorithm for this problem, MaxAdaptiveDegree(MAD), which adaptively reroutes weight from items with weight far above the threshold needed for privacy to items with smaller weight, thereby increasing the probability that less frequent items are output. Our algorithm can be efficiently implemented in massively parallel computation systems allowing scalability to very large datasets. We prove that our algorithm stochastically dominates the standard parallel algorithm for this problem. We also develop a two-round version of our algorithm, MAD2R, where results of the computation in the first round are used to bias the weighting in the second round to maximize the number of items output. In experiments, our algorithms provide the best results across the board among parallel algorithms and scale to datasets with hundreds of billions of items, up to three orders of magnitude larger than those analyzed by prior sequential algorithms.
View details
Silent Data Corruption by 10× Test Escapes Threatens Reliable Computing
Rama Govindaraju
Eric Liu
Subhasish Mitra
Mike Fuller
IEEE (2025) (to appear)
Preview abstract
Summary:
Silent Data Corruption by 10x Test Escapes Threatens Reliable Computing" highlights a critical issue: manufacturing defects, dubbed "test escapes," are evading current testing methods at an alarming rate, ten times higher than industry targets. These defects lead to Silent Data Corruption (SDC), where applications produce incorrect outputs without error indications, costing companies significantly in debugging, data recovery, and service disruptions. The paper proposes a three-pronged approach: quick diagnosis of defective chips directly from system-level behaviors, in-field detection using advanced testing and error detection techniques like CASP, and new, rigorous test experiments to validate these solutions and improve manufacturing testing practices.
View details
Data Quality Issues in Multilingual Speech Datasets: The Need for Sociolinguistic Awareness and Proactive Language Planning
Preview
Mingfei Lau
Allen Chen
Yeming Fang
Tingting Xu
Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics, Association for Computational Linguistics (ACL), Vienna, Austria (2025), 7466–7492
User-Centered Delivery of AI-Powered Health Care Technologies in Clinical Settings: Mixed Methods Case Study
Randall Brandt
Hien Brown
Christine Silva
JMIR Human Factors (2025)
Preview abstract
Background:
Providers spend a large percentage of their day using electronic health record (EHR) technology and frequently report frustration when EHR tasks are time-consuming and effortful. To solve these challenges, artificial intelligence (AI)–based enhancements to EHR technology are increasingly being deployed. However, AI-based implementations for EHR features often lack user-centered evaluation.
Objective:
This study evaluates, using a user-centered approach, the implementation of an AI-powered search and clinical discovery tool within an EHR system.
Methods:
We conducted a mixed methods study consisting of interviews, observations, and surveys for 5 months.
Results:
High adoption rates for the AI-based features (163/176, 93% users after 3 months) and significant increases across key metrics, including user satisfaction (U=49; P<.001) and perception of time saved (U=49; P<.001), demonstrated that the AI-based features were not only successfully integrated into various clinical workflows but also improved the user experience for clinicians.
Conclusions:
Our results underscore the feasibility and effectiveness of using a user-centered approach for the deployment of clinical AI tools. High adoption rates and positive user experiences were driven by our user-centered research program, which emphasized close collaboration with users, rapid incorporation of feedback, and tailored user training. This study program can be used as a starting framework for the design and integration of human-centered research methods for AI tool deployment in clinical settings.
View details
AfriMed-QA: A Pan-African Multi-Specialty Medical Question-Answering Benchmark Dataset
Tobi Olatunji
Abraham Toluwase Owodunni
Charles Nimo
Jennifer Orisakwe
Henok Biadglign Ademtew
Chris Fourie
Foutse Yuehgoh
Stephen Moore
Mardhiyah Sanni
Emmanuel Ayodele
Timothy Faniran
Bonaventure F. P. Dossou
Fola Omofoye
Wendy Kinara
Tassallah Abdullahi
Michael Best
2025
Preview abstract
Recent advancements in large language model (LLM) performance on medical multiple-choice question (MCQ) benchmarks have stimulated significant interest from patients and healthcare providers globally. Particularly in low- and middle-income countries (LMICs) facing acute physician shortages and lack of specialists, LLMs offer a potentially scalable pathway to enhance healthcare access and reduce costs. However, LLM training data is sourced from predominantly Western text, existing benchmarks are predominantly Western-centric, limited to MCQs, and focused on a narrow range of clinical specialties, raising concerns about their applicability in the Global South, particularly across Africa where localized medical knowledge and linguistic diversity are often underrepresented. In this work, we introduce AfriMed-QA, the first large-scale multi-specialty Pan-African medical Question-Answer (QA) dataset designed to evaluate and develop equitable and effective LLMs for African healthcare. It contains 3,000 multiple-choice professional medical exam questions with answers and rationale, 1,500 short answer questions (SAQ) with long-from answers, and 5,500 consumer queries, sourced from over 60 medical schools across 15 countries, covering 32 medical specialties. We further rigorously evaluate multiple open, closed, general, and biomedical LLMs across multiple axes including accuracy, consistency, factuality, bias, potential for harm, local geographic relevance, medical reasoning, and recall. We believe this dataset provides a valuable resource for practical application of large language models in African healthcare and enhances the geographical diversity of health-LLM benchmark datasets.
View details
Improving simulation-based origin-destination demand calibration using sample segment counts data
Arwa Alanqary
Yechen Li
The 12th Triennial Symposium on Transportation Analysis conference (TRISTAN XII), Okinawa, Japan (2025)
Preview abstract
This paper introduces a novel approach to demand estimation that utilizes partial observations of segment-level track counts. Building on established simulation-based demand estimation methods, we present a modified formulation that integrates sample track counts as a regularization term. This approach effectively addresses the underdetermination challenge in demand estimation, moving beyond the conventional reliance on a prior OD matrix. The proposed formulation aims to preserve the distribution of the observed track counts while optimizing the demand to align with observed path-level travel times. We tested this approach on Seattle's highway network with various congestion levels. Our findings reveal significant enhancements in the solution quality, particularly in accurately recovering ground truth demand patterns at both the OD and segment levels.
View details
Procurement Auctions via Approximate Submodular Optimization
Amin Karbasi
Grigoris Velegkas
Forty-second International Conference on Machine Learning (2025)
Preview abstract
We study the problem of procurement auctions, in which an auctioneer seeks to acquire services from a group of strategic sellers with private costs. The quality of the services is measured through some \emph{submodular} function that is known to the auctioneer. Our goal is to design \emph{computationally efficient} procurement auctions that (approximately) maximize the difference between the quality of the acquired services and the total cost of the sellers, in a way that is incentive compatible (IC) and individual rational (IR) for the sellers, and generates non-negative surplus (NAS) for the auctioneer.
Leveraging recent results from the literature of \emph{non-positive} submodular function maximization, we design computationally efficient frameworks that transform submodular function optimization algorithms to \emph{mechanisms} that are IC and IR for the sellers, NAS for the auctioneer, and \emph{approximation-preserving}. Our frameworks are general and work both in the \emph{offline} setting where the auctioneer can observe the bids and the services of all the sellers simultaneously, and in the \emph{online} setting where the sellers arrive in an adversarial order and the auctioneer has to make an irrevocable decision whether to purchase their service or not. We further investigate whether it is possible to convert state-of-art submodular optimization algorithms into a descending auction. We focurs in the adversarial setting, meaning that the schedule of the descending prices is determined by an advesary. We show that a submodular optimization algorithm satisfying bi-criteria $(\alpha, 1)$-approximation in welfare can be effectively converted to a descending auction in the adversarial setting in if and only if $\alpha \leq \frac 1 2$. Our result highlights the importance of a carefully designed schedule of descending prices to effectively convert a submodular optimization algorithm satisfying bi-criteria $(\alpha, 1)$-approximation in welfare with $\alpha > \frac 1 2$ to a descending auction. We also further establish a connection between descending auctions and online submodular optimization algorithms.
We demonstrate the practical applications of our frameworks by instantiating them with different state-of-the-art submodular optimization algorithms and comparing their welfare performance through empirical experiments on publicly available datasets that consist of thousands of sellers.
View details
Leveraging Per-Example Privacy for Machine Unlearning
Nazanin Mohammadi Sepahvand
Anvith Thudi
Ashmita Bhattacharyya
Nicolas Papernot
Eleni Triantafillou
Daniel M. Roy
Karolina Dziugaite
International Conference on Machine Learning (ICML) (2025)
Preview abstract
This work focuses on developing fine-grained theoretical insights to quantify unlearning difficulty at the level of individual data points for fine-tuning-based unlearning. Unlike other unlearning methods that lack theoretical guarantees for non-convex models, our approach builds on recent advances in differential privacy to provide per-instance guarantees using Rényi divergence. While our theoretical analysis applies to Langevin dynamics, we empirically demonstrate that the derived guarantees—and their trends—continue to hold for fine-tuning, even in the absence of explicit noise. Our results show that per-instance privacy levels computed from training dynamics reliably predict unlearning difficulty, offering a principled and practical way to assess unlearning performance. Furthermore, our method identifies harder-to-unlearn data more effectively than existing heuristics, providing a more precise tool for guiding unlearning strategies. These findings pave the way for adaptive and efficient unlearning methods tailored to the properties of specific data points.
View details
SSDTrain: Faster Large Language Model Training Using SSD-Based Activation Offloading
Kun Wu
Jeongmin Brian Park
Mert Hidayetoğlu
Vikram Sharma Mailthody
Sitao Huang
Steven Lumetta
Wen-mei Hwu
Design Automation Conference (DAC) (2025)
Preview abstract
The scaling up of Large Language Models (LLMs) demands more memory than current GPUs can provide, hindering the training process. To address this challenge, we propose SSDTrain to efficiently offload activations, the intermediate tensors produced during LLM training, to SSDs. This approach reduces GPU memory usage without impacting performance by adaptively overlapping data transfers with computation. SSDTrain is compatible with popular deep learning frameworks like PyTorch, Megatron, and DeepSpeed, and it employs techniques such as tensor deduplication, forwarding, and adaptive offloading to further enhance efficiency. We conduct extensive experiments on Llama, BERT, and T5. Results demonstrate that SSDTrain effectively reduces 45% of the activation peak memory usage. It can perfectly overlap the IO with the computation without introducing performance penalty. SSDTrain can achieve a performance boost of up to 31% compared to the conventional training strategy using the same GPU systems.
View details
